Turkiye Klinikleri Journal of Dental Sciences

.: ORIGINAL RESEARCH
Derin Öğrenme Yöntemi Kullanılarak Geliştirilen Yapay Zekâ Yöntemi ile Panoramik Radyografilerde Dental Restorasyonların Otomatik Tespiti ve Sınıflandırılması: Metodolojik Çalışmalar
Automatic Detection and Classification of Dental Restorations in Panoramic Radiographs with Artificial Intelligence Method Developed Using Deep Learning Method: Methodological Studies
Esra AŞÇIa, Münevver KILIÇa, Özer ÇELİKb,c, İbrahim Şevki BAYRAKDARb,d, Elif BİLGİRb, Ahmet Faruk ASLANc, Alper ODABAŞc, Kaan ORHANe
aAtatürk Üniversitesi Diş Hekimliği Fakültesi, Çocuk Diş Hekimliği ABD, Erzurum, Türkiye
bEskişehir Osmangazi Üniversitesi, Sağlıkta Bilgisayar Destekli Tanı ve Tedavi Araştırma ve Uygulama Merkezi, Eskişehir, Türkiye
cEskişehir Osmangazi Üniversitesi Fen Fakültesi, Matematik ve Bilgisayar Bilimleri Bölümü, Eskişehir, Türkiye
dEskişehir Osmangazi Üniversitesi Diş Hekimliği Fakültesi, Oral ve Maksillofasiyal Radyoloji ABD, Eskişehir, Türkiye
eAnkara Üniversitesi Diş Hekimliği Fakültesi, Oral ve Maksillofasiyal Radyoloji ABD, Ankara, Türkiye
Turkiye Klinikleri J Dental Sci. 2022;28(2):329-37
doi: 10.5336/dentalsci.2021-84835
Article Language: TR
Full Text
ÖZET
 Amaç: Bu çalışmanın amacı, derin öğrenme yöntemi kullanılarak geliştirilen yapay zekâ yöntemi ile panoramik radyografilerde dental restorasyonları otomatik olarak tespit etmek ve sınıflandırmaktır. Gereç ve Yöntemler: Bu çalışmada, yapay zekâ modeli geliştirmek için 12-15 yaş aralığındaki çocuklardan alınan 789 panoramik radyografi kullanılmıştır. Radyografiler, Atatürk Üniversitesi Diş Hekimliği Fakültesi Pedodonti ABD radyoloji arşivinden elde edilmiştir. Çalışmamıza dâhil edilen panoramik radyografiler dolgu ve kanal tedavisi olmak üzere 2 gruba ayrılmıştır. PyTorch kütüphanesi ile uygulanan U-Net modeli restoratif materyallerin tespiti ve segmentasyonu için kullanılmıştır. Yapay zekâ performansı, karışıklık matrisi kullanılarak değerlendirilmiştir. Bulgular: Yapay zekâ sistemi dolgu grubuna ait test grubunda; 50 görüntüde bulunan 94 dolgulu diş etiket sayısından 89'unu gerçek pozitif, 1'ini yanlış pozitif ve 4'ünü yanlış negatif olarak değerlendirmiştir. Karışıklık matrisi kullanarak hesaplanan duyarlılık, kesinlik ve F1 skor değerleri sırasıyla 0,9569, 0,9888 ve 0,9726 olarak tespit edilmiştir. Kanal tedavisi, test grubunda 40 görüntüde bulunan 76 kanal tedavili diş etiket sayısından 60'ı gerçek pozitif, 0 yanlış pozitif ve 11'i yanlış negatif olarak değerlendirmiştir. Karışıklık matrisi kullanarak hesaplanan duyarlılık, kesinlik ve F1 değerleri sırasıyla 0,8450, 1 ve 0,9160 olarak tespit edilmiştir. Sonuç: Derin öğrenme tabanlı yapay zekâ modelleri, daimî dişlenme dönemine ait çocuklardan alınan panoramik radyografilerde restorasyonları otomatik olarak tespitinde çok iyi performans göstermiştir. Yapay zekâ araçları, klinisyenlere zaman kazandırabilir ve karar destek sistemi olarak yardımcı olabilir.

Anahtar Kelimeler: Yapay zekâ; derin öğrenme; pedodonti; dental restorasyon
ABSTRACT
 Objective: This study aims to detect and classify it with the artificial intelligence method developed using the deep learning method. Material and Methods: In this study, 789 panoramic radiographs taken from children aged 12-15 were used to develop an artificial intelligence model. Radiographs were obtained from the radiology archive of Atatürk University Faculty of Dentistry, Department of Pedodontics. Panoramic radiographs included in our study were divided into two groups as filling and root canal treatment. The U-Net model implemented with the PyTorch library was used for the detection and segmentation of restorative materials. AI performance was evaluated using the confusion matrix. Results: Artificial intelligence system; in the test group belonging to the filler group; evaluated 89 true positives, 1 false-positive, and 4 false negatives out of 94 filled tooth tags in 50 images. Sensitivity, precision, and F1 score values calculated using the confusion matrix were found to be 0.9569, 0.9888, and 0.9726, respectively. Root canal treatment evaluated 60 as true positive, 0 false positive, and 11 false negatives out of 76 root canal-treated tooth tags in 40 images in the test group. Sensitivity, precision, and F1 values calculated using the confusion matrix were found to 0,8450, 1 ve and 0,9160, respectively. Conclusion: Deep learning-based artificial intelligence models have performed very well in automatically detecting restorations in panoramic radiographs of children with permanent dentition. AI tools can save clinicians time and assist as a decision support system.

Keywords: Artificial intelligence; deep learning; pediatric dentistry; dental restoration
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